Model comparison
Grok 4.6 vs Llama 3-8B
Grok 4.6 is the stronger model overall, scoring 56.8 to 25.3 on the Noometry Index. Llama 3-8B costs 21× less per token, which makes it the better buy when Grok 4.6's lead doesn't matter for your workload.
Last verified . 22 shared benchmarks.
MetaSummary
- They share 22 benchmarks with published results for both. Grok 4.6 scores higher in 8 categories and Llama 3-8B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Grok 4.6 leads 66.8 to 8.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 99.2% for Grok 4.6 and 1.9% for Llama 3-8B.
- Llama 3-8B is cheaper at $0.14 / $0.14 per million input/output tokens, against $2 / $6 for Grok 4.6.
- Grok 4.6 accepts more context: 500K tokens versus 8K.
- Llama 3-8B has downloadable open weights; the other is API-only.
Side by side
| Grok 4.6 | Llama 3-8B | |
|---|---|---|
| Provider | xAI | Meta |
| Noometry Index | 56.8 | 25.3 |
| Released | 2026-08-12 | 2024-04-18 |
| Weights | Proprietary | Open |
| Context window | 500K | 8K |
| Max output | 500K | 8K |
| Input $ / M tokens | $2 | $0.14 |
| Output $ / M tokens | $6 | $0.14 |
| Results tracked | 49 | 34 |
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Category by category
Coding Grok 4.6 leads
Grok 4.6: 58.0 (#14), Llama 3-8B: 30.5 (#285)
| Benchmark | Grok 4.6 | Llama 3-8B |
|---|---|---|
| LMArena Coding | 1464 | 1152 |
| DeepSWE | 67.5% | — |
| FrontierCode | 48% | — |
| CursorBench | 41.4% | — |
| LMArena WebDev | 1620 | — |
| FrontierSWE | 25.3% | — |
| SciCode | 56.5% | — |
| WeirdML | 67.3% | — |
| BigCodeBench Instruct | — | 31.9% |
| BigCodeBench Complete | — | 36.9% |
| ALE-Bench | 1,508 | — |
| HumanEval+ | — | 56.7% |
| MBPP+ | — | 54.8% |
Agentic & Tool Use Not comparable
Grok 4.6: 39.4 (#25), Llama 3-8B: —
| Benchmark | Grok 4.6 | Llama 3-8B |
|---|---|---|
| APEX-Agents | 65.3% | — |
| GDP.pdf | 17.2% | — |
| Vending-Bench 2 | 9,047 | — |
Reasoning Grok 4.6 leads
Grok 4.6: 61.3 (#18), Llama 3-8B: 13.8 (#321)
| Benchmark | Grok 4.6 | Llama 3-8B |
|---|---|---|
| Chess Puzzles | 40% | 0% |
| LMArena Hard Prompts | 1445 | 1133 |
| DTBench | 97.3% | 43.9% |
| Epoch Capabilities Index | 156.48 | 116.33 |
| ARC-AGI-2 | 67.1% | — |
| SimpleBench | 75.9% | — |
| NYT Connections (extended) | 80% | — |
| ARC-AGI-1 | 87.5% | — |
| CritPt | 19.7% | — |
| EBR-Bench | 30.5% | — |
| Mystery Game Puzzles | 34% | — |
| LMCA | 48.5% | — |
| Adversarial NLI | — | 57.3% |
| ForecastBench | — | 58.6 |
| WinoGrande | — | 75.7% |
Math Grok 4.6 leads
Grok 4.6: 66.8 (#19), Llama 3-8B: 8.3 (#317)
| Benchmark | Grok 4.6 | Llama 3-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 99.2% | 1.9% |
| LMArena Math | 1416 | 1151 |
| FrontierMath (Tiers 1-3) | 66% | — |
| FrontierMath Tier 4 | 31.7% | — |
| ProofBench | 51% | — |
| MATH Level 5 | — | 6.1% |
Knowledge Grok 4.6 leads
Grok 4.6: 63.3 (#18), Llama 3-8B: 7.8 (#303)
| Benchmark | Grok 4.6 | Llama 3-8B |
|---|---|---|
| GPQA Diamond | 94% | 26.1% |
| LMArena Expert | 1463 | 1113 |
| SimpleQA Verified | 49.3% | — |
| ARC (AI2) Challenge | — | 82.8% |
| MMLU | — | 68.8% |
| OpenBookQA | — | 82.6% |
| TriviaQA | — | 67.7% |
Multimodal Not comparable
Grok 4.6: 43.9 (#18), Llama 3-8B: —
| Benchmark | Grok 4.6 | Llama 3-8B |
|---|---|---|
| LMArena Vision | 1265 | — |
| Blueprint-Bench 2 | 33.2% | — |
| Furniture Assembly | 40% | — |
| LMArena Document | 1452 | — |
Multilingual Grok 4.6 leads
Grok 4.6: 53.3 (#67), Llama 3-8B: 31.0 (#256)
| Benchmark | Grok 4.6 | Llama 3-8B |
|---|---|---|
| LMArena Non-English | 1422 | 1098 |
| LMArena Chinese | 1479 | 1076 |
| LMArena French | 1458 | 1158 |
| LMArena German | 1425 | 1104 |
| LMArena Japanese | 1383 | 967 |
| LMArena Korean | 1395 | 1003 |
| LMArena Russian | 1427 | 1109 |
| LMArena Spanish | 1408 | 1172 |
Instruction Following Grok 4.6 leads
Grok 4.6: 75.6 (#59), Llama 3-8B: 58.7 (#255)
| Benchmark | Grok 4.6 | Llama 3-8B |
|---|---|---|
| LMArena Instruction Following | 1431 | 1127 |
Long Context Grok 4.6 leads
Grok 4.6: 44.4 (#62), Llama 3-8B: 34.2 (#246)
| Benchmark | Grok 4.6 | Llama 3-8B |
|---|---|---|
| LMArena Longer Query | 1453 | 1128 |
Writing & Preference Grok 4.6 leads
Grok 4.6: 62.5 (#77), Llama 3-8B: 37.7 (#251)
| Benchmark | Grok 4.6 | Llama 3-8B |
|---|---|---|
| LMArena Text | 1427 | 1166 |
| LMArena Creative Writing | 1430 | 1150 |
| LMArena Multi-Turn | 1422 | 1152 |
Frequently asked questions
Is Grok 4.6 better than Llama 3-8B?
Grok 4.6 is the stronger model overall, scoring 56.8 to 25.3 on the Noometry Index. Llama 3-8B costs 21× less per token, which makes it the better buy when Grok 4.6's lead doesn't matter for your workload.
Which is cheaper, Grok 4.6 or Llama 3-8B?
Llama 3-8B is cheaper. It lists at $0.14 per million input tokens and $0.14 per million output tokens; Grok 4.6 lists at $2 and $6.
Is Grok 4.6 or Llama 3-8B better for coding?
Grok 4.6 scores higher on coding benchmarks: 58.0 versus 30.5 in the Noometry coding category.
Which has the bigger context window?
Grok 4.6 does, with 500K tokens against 8K.
How many benchmarks do Grok 4.6 and Llama 3-8B share?
22 benchmarks have published results for both models. Grok 4.6 has 49 scored results on Noometry and Llama 3-8B has 34.